Overview

Dataset statistics

Number of variables10
Number of observations2117
Missing cells14
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.2 KiB
Average record size in memory145.7 B

Variable types

Categorical1
Numeric9

Warnings

Name has a high cardinality: 2114 distinct values High cardinality
Alter is highly correlated with Anzahl der KinderHigh correlation
Erbwerbstätige (15-64) is highly correlated with Anzahl an EhepaarenHigh correlation
Anzahl an Ehepaaren is highly correlated with Erbwerbstätige (15-64)High correlation
Anzahl der Kinder is highly correlated with AlterHigh correlation
Alter is highly correlated with Erbwerbstätige (15-64) and 1 other fieldsHigh correlation
Erbwerbstätige (15-64) is highly correlated with AlterHigh correlation
Anzahl der Kinder is highly correlated with AlterHigh correlation
Alter is highly correlated with Anzahl der KinderHigh correlation
Erbwerbstätige (15-64) is highly correlated with Anzahl an Ehepaaren and 1 other fieldsHigh correlation
Anzahl der Kinder is highly correlated with AlterHigh correlation
Einwohner is highly correlated with GrundstückspreiseHigh correlation
Einkommen is highly correlated with Beschäftigte and 1 other fieldsHigh correlation
Beschäftigte is highly correlated with Einkommen and 1 other fieldsHigh correlation
Anzahl an Ehepaaren is highly correlated with Erbwerbstätige (15-64) and 1 other fieldsHigh correlation
Grundstückspreise is highly correlated with EinwohnerHigh correlation
Arbeitsstätten is highly correlated with Erbwerbstätige (15-64) and 3 other fieldsHigh correlation
Erbwerbstätige (15-64) is highly skewed (γ1 = 34.04456658) Skewed
Anzahl an Ehepaaren is highly skewed (γ1 = 34.91692343) Skewed
Name is uniformly distributed Uniform

Reproduction

Analysis started2021-06-18 13:33:26.542049
Analysis finished2021-06-18 13:33:50.319571
Duration23.78 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2114
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size152.3 KiB
Warth
 
2
Mühldorf
 
2
Krumbach
 
2
Leibnitz
 
1
Stattegg
 
1
Other values (2109)
2109 

Length

Max length37
Median length10
Mean length12.49692962
Min length2

Characters and Unicode

Total characters26456
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2111 ?
Unique (%)99.7%

Sample

1st rowGramais
2nd rowNamlos
3rd rowTschanigraben
4th rowKaisers
5th rowHinterhornbach

Common Values

ValueCountFrequency (%)
Warth2
 
0.1%
Mühldorf2
 
0.1%
Krumbach2
 
0.1%
Leibnitz1
 
< 0.1%
Stattegg1
 
< 0.1%
Maria Neustift1
 
< 0.1%
Kaindorf1
 
< 0.1%
Mauthausen1
 
< 0.1%
Stinatz1
 
< 0.1%
Jenbach1
 
< 0.1%
Other values (2104)2104
99.4%

Length

2021-06-18T15:33:51.019401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
am159
 
4.6%
im125
 
3.6%
der123
 
3.6%
an111
 
3.2%
st87
 
2.5%
bei59
 
1.7%
sankt52
 
1.5%
bad34
 
1.0%
in31
 
0.9%
see28
 
0.8%
Other values (2087)2655
76.6%

Most occurring characters

ValueCountFrequency (%)
e2821
 
10.7%
r2142
 
8.1%
n2075
 
7.8%
a2071
 
7.8%
i1502
 
5.7%
1347
 
5.1%
t1321
 
5.0%
l1100
 
4.2%
h1009
 
3.8%
s1007
 
3.8%
Other values (50)10061
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21850
82.6%
Uppercase Letter3003
 
11.4%
Space Separator1347
 
5.1%
Dash Punctuation157
 
0.6%
Other Punctuation97
 
0.4%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2821
12.9%
r2142
 
9.8%
n2075
 
9.5%
a2071
 
9.5%
i1502
 
6.9%
t1321
 
6.0%
l1100
 
5.0%
h1009
 
4.6%
s1007
 
4.6%
d812
 
3.7%
Other values (19)5990
27.4%
Uppercase Letter
ValueCountFrequency (%)
S456
15.2%
G224
 
7.5%
W224
 
7.5%
M222
 
7.4%
K177
 
5.9%
P175
 
5.8%
H173
 
5.8%
L162
 
5.4%
A154
 
5.1%
B149
 
5.0%
Other values (16)887
29.5%
Dash Punctuation
ValueCountFrequency (%)
-157
100.0%
Other Punctuation
ValueCountFrequency (%)
.97
100.0%
Space Separator
ValueCountFrequency (%)
1347
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin24853
93.9%
Common1603
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2821
 
11.4%
r2142
 
8.6%
n2075
 
8.3%
a2071
 
8.3%
i1502
 
6.0%
t1321
 
5.3%
l1100
 
4.4%
h1009
 
4.1%
s1007
 
4.1%
d812
 
3.3%
Other values (45)8993
36.2%
Common
ValueCountFrequency (%)
1347
84.0%
-157
 
9.8%
.97
 
6.1%
(1
 
0.1%
)1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26067
98.5%
Latin 1 Sup389
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2821
 
10.8%
r2142
 
8.2%
n2075
 
8.0%
a2071
 
7.9%
i1502
 
5.8%
1347
 
5.2%
t1321
 
5.1%
l1100
 
4.2%
h1009
 
3.9%
s1007
 
3.9%
Other values (44)9672
37.1%
Latin 1 Sup
ValueCountFrequency (%)
ö148
38.0%
ü103
26.5%
ß95
24.4%
ä39
 
10.0%
Ü3
 
0.8%
Ö1
 
0.3%

Einwohner
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1730
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4219.491734
Minimum41
Maximum291134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:51.492615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile487.4
Q11150
median1842
Q33251
95-th percentile10579.8
Maximum291134
Range291093
Interquartile range (IQR)2101

Descriptive statistics

Standard deviation14056.54905
Coefficient of variation (CV)3.331337027
Kurtosis169.3898221
Mean4219.491734
Median Absolute Deviation (MAD)894
Skewness11.68717135
Sum8932664
Variance197586571.1
MonotonicityIncreasing
2021-06-18T15:33:51.867195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6105
 
0.2%
12595
 
0.2%
14405
 
0.2%
7674
 
0.2%
6864
 
0.2%
12404
 
0.2%
11324
 
0.2%
15924
 
0.2%
10553
 
0.1%
14043
 
0.1%
Other values (1720)2076
98.1%
ValueCountFrequency (%)
411
< 0.1%
651
< 0.1%
671
< 0.1%
781
< 0.1%
941
< 0.1%
991
< 0.1%
1051
< 0.1%
1061
< 0.1%
1271
< 0.1%
1401
< 0.1%
ValueCountFrequency (%)
2911341
< 0.1%
2105731
< 0.1%
2065371
< 0.1%
1988061
< 0.1%
1739161
< 0.1%
1554161
< 0.1%
1310591
< 0.1%
1118121
< 0.1%
1052371
< 0.1%
1050221
< 0.1%

Arbeitsstätten
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2079
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09323908927
Minimum0.01492537313
Maximum0.7667485977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:52.337490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01492537313
5-th percentile0.0589159829
Q10.07468519323
median0.08863198459
Q30.104213865
95-th percentile0.1402104355
Maximum0.7667485977
Range0.7518232246
Interquartile range (IQR)0.02952867175

Descriptive statistics

Standard deviation0.0356942701
Coefficient of variation (CV)0.3828251689
Kurtosis124.3263444
Mean0.09323908927
Median Absolute Deviation (MAD)0.01458804642
Skewness7.797778255
Sum197.387152
Variance0.001274080918
MonotonicityNot monotonic
2021-06-18T15:33:52.732022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.090909090914
 
0.2%
0.13
 
0.1%
0.10526315793
 
0.1%
0.1065573773
 
0.1%
0.076923076923
 
0.1%
0.086956521742
 
0.1%
0.085106382982
 
0.1%
0.064666103132
 
0.1%
0.11111111112
 
0.1%
0.094117647062
 
0.1%
Other values (2069)2091
98.8%
ValueCountFrequency (%)
0.014925373131
< 0.1%
0.026268115941
< 0.1%
0.027419354841
< 0.1%
0.030789825971
< 0.1%
0.032305433191
< 0.1%
0.039301310041
< 0.1%
0.041467502051
< 0.1%
0.041822255411
< 0.1%
0.041951219511
< 0.1%
0.042909090911
< 0.1%
ValueCountFrequency (%)
0.76674859771
< 0.1%
0.75438596491
< 0.1%
0.45939393941
< 0.1%
0.37142857141
< 0.1%
0.35483870971
< 0.1%
0.290043291
< 0.1%
0.27584020291
< 0.1%
0.27272727271
< 0.1%
0.26561427591
< 0.1%
0.25046904321
< 0.1%

Beschäftigte
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2103
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3772212097
Minimum0.01492537313
Maximum10.68557383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:53.111297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01492537313
5-th percentile0.1474250754
Q10.2284644195
median0.316271722
Q30.4510638298
95-th percentile0.8114247073
Maximum10.68557383
Range10.67064846
Interquartile range (IQR)0.2225994103

Descriptive statistics

Standard deviation0.3155814396
Coefficient of variation (CV)0.8365951635
Kurtosis541.294343
Mean0.3772212097
Median Absolute Deviation (MAD)0.0999395713
Skewness17.39735337
Sum798.5773009
Variance0.09959164502
MonotonicityNot monotonic
2021-06-18T15:33:53.477751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.33333333333
 
0.1%
0.32857142862
 
0.1%
0.30184804932
 
0.1%
0.25711619232
 
0.1%
0.11532258062
 
0.1%
0.29032258062
 
0.1%
0.22159090912
 
0.1%
0.19823788552
 
0.1%
0.39473684212
 
0.1%
0.24022346372
 
0.1%
Other values (2093)2096
99.0%
ValueCountFrequency (%)
0.014925373131
< 0.1%
0.055800293691
< 0.1%
0.061135371181
< 0.1%
0.06251
< 0.1%
0.074275362321
< 0.1%
0.075242718451
< 0.1%
0.076923076921
< 0.1%
0.084745762711
< 0.1%
0.089156626511
< 0.1%
0.089876033061
< 0.1%
ValueCountFrequency (%)
10.685573831
< 0.1%
3.1860360361
< 0.1%
1.922244761
< 0.1%
1.7212288691
< 0.1%
1.5753943221
< 0.1%
1.5312883441
< 0.1%
1.4868913861
< 0.1%
1.4836431231
< 0.1%
1.4510204081
< 0.1%
1.4229665071
< 0.1%

Alter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct854
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.95324043
Minimum37.79
Maximum55.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:54.022972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37.79
5-th percentile40.29
Q142.18
median43.75
Q345.52
95-th percentile48.29
Maximum55.56
Range17.77
Interquartile range (IQR)3.34

Descriptive statistics

Standard deviation2.464286724
Coefficient of variation (CV)0.05606609888
Kurtosis0.4412382813
Mean43.95324043
Median Absolute Deviation (MAD)1.66
Skewness0.5035243957
Sum93049.01
Variance6.07270906
MonotonicityNot monotonic
2021-06-18T15:33:54.533348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.6910
 
0.5%
42.829
 
0.4%
43.818
 
0.4%
44.138
 
0.4%
44.878
 
0.4%
44.938
 
0.4%
41.947
 
0.3%
44.647
 
0.3%
43.237
 
0.3%
44.757
 
0.3%
Other values (844)2038
96.3%
ValueCountFrequency (%)
37.791
< 0.1%
37.931
< 0.1%
37.961
< 0.1%
38.032
0.1%
38.081
< 0.1%
38.111
< 0.1%
38.191
< 0.1%
38.241
< 0.1%
38.31
< 0.1%
38.331
< 0.1%
ValueCountFrequency (%)
55.561
< 0.1%
53.631
< 0.1%
53.561
< 0.1%
53.381
< 0.1%
53.071
< 0.1%
52.681
< 0.1%
52.211
< 0.1%
51.951
< 0.1%
51.891
< 0.1%
51.691
< 0.1%

Einkommen
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1941
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.72307038
Minimum27.173
Maximum76.668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:54.960807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum27.173
5-th percentile35.3186
Q137.831
median40.094
Q342.825
95-th percentile47.8838
Maximum76.668
Range49.495
Interquartile range (IQR)4.994

Descriptive statistics

Standard deviation4.343363584
Coefficient of variation (CV)0.106656093
Kurtosis7.302586122
Mean40.72307038
Median Absolute Deviation (MAD)2.444
Skewness1.704758961
Sum86210.74
Variance18.86480722
MonotonicityNot monotonic
2021-06-18T15:33:55.343868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.0063
 
0.1%
41.3373
 
0.1%
39.8283
 
0.1%
39.7013
 
0.1%
41.9033
 
0.1%
41.5243
 
0.1%
38.0253
 
0.1%
41.2083
 
0.1%
42.0253
 
0.1%
42.3773
 
0.1%
Other values (1931)2087
98.6%
ValueCountFrequency (%)
27.1731
< 0.1%
27.4081
< 0.1%
29.1071
< 0.1%
30.8651
< 0.1%
31.2081
< 0.1%
31.2941
< 0.1%
32.1251
< 0.1%
32.7341
< 0.1%
32.8171
< 0.1%
32.9491
< 0.1%
ValueCountFrequency (%)
76.6681
< 0.1%
72.8471
< 0.1%
70.7891
< 0.1%
67.3871
< 0.1%
65.1471
< 0.1%
62.8131
< 0.1%
61.9341
< 0.1%
61.811
< 0.1%
60.2751
< 0.1%
60.1641
< 0.1%

Erbwerbstätige (15-64)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2088
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4991826862
Minimum0.1403985507
Maximum4.426900585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:55.741730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.1403985507
5-th percentile0.4352251403
Q10.4743589744
median0.5007208073
Q30.5226890756
95-th percentile0.55197369
Maximum4.426900585
Range4.286502034
Interquartile range (IQR)0.04833010127

Descriptive statistics

Standard deviation0.09448984531
Coefficient of variation (CV)0.1892891078
Kurtosis1413.027666
Mean0.4991826862
Median Absolute Deviation (MAD)0.02343058406
Skewness34.04456658
Sum1056.769747
Variance0.008928330867
MonotonicityNot monotonic
2021-06-18T15:33:56.081420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.46666666673
 
0.1%
0.51944444443
 
0.1%
0.53
 
0.1%
0.51851851853
 
0.1%
0.53255813952
 
0.1%
0.52765957452
 
0.1%
0.5686274512
 
0.1%
0.54337592472
 
0.1%
0.55555555562
 
0.1%
0.50943396232
 
0.1%
Other values (2078)2093
98.9%
ValueCountFrequency (%)
0.14039855071
< 0.1%
0.30456407261
< 0.1%
0.3248299321
< 0.1%
0.3411611221
< 0.1%
0.34354485781
< 0.1%
0.35053763441
< 0.1%
0.35899450121
< 0.1%
0.35984848481
< 0.1%
0.37153196621
< 0.1%
0.37417218541
< 0.1%
ValueCountFrequency (%)
4.4269005851
< 0.1%
1.121951221
< 0.1%
0.64646464651
< 0.1%
0.64615384621
< 0.1%
0.61635220131
< 0.1%
0.61259541981
< 0.1%
0.60606060611
< 0.1%
0.60383189121
< 0.1%
0.5958904111
< 0.1%
0.59389213781
< 0.1%

Grundstückspreise
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1024
Distinct (%)48.7%
Missing14
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean125.9080837
Minimum7.8
Maximum1712.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:56.417731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7.8
5-th percentile15.7
Q137.6
median68.4
Q3154.75
95-th percentile416.66
Maximum1712.7
Range1704.9
Interquartile range (IQR)117.15

Descriptive statistics

Standard deviation154.8397709
Coefficient of variation (CV)1.229784192
Kurtosis20.44302604
Mean125.9080837
Median Absolute Deviation (MAD)41.5
Skewness3.546779912
Sum264784.7
Variance23975.35465
MonotonicityNot monotonic
2021-06-18T15:33:56.854265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.214
 
0.7%
4312
 
0.6%
2212
 
0.6%
11.911
 
0.5%
13.611
 
0.5%
51.310
 
0.5%
41.610
 
0.5%
14.19
 
0.4%
35.99
 
0.4%
46.59
 
0.4%
Other values (1014)1996
94.3%
(Missing)14
 
0.7%
ValueCountFrequency (%)
7.85
0.2%
81
 
< 0.1%
9.31
 
< 0.1%
10.84
 
0.2%
10.93
 
0.1%
116
0.3%
11.53
 
0.1%
11.911
0.5%
12.57
0.3%
12.61
 
< 0.1%
ValueCountFrequency (%)
1712.71
< 0.1%
16691
< 0.1%
15601
< 0.1%
1186.41
< 0.1%
1161.41
< 0.1%
1102.81
< 0.1%
1066.21
< 0.1%
1052.31
< 0.1%
10431
< 0.1%
1006.31
< 0.1%

Anzahl an Ehepaaren
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2068
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2072855374
Minimum0.0606884058
Maximum2.152046784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:57.270890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0606884058
5-th percentile0.1781263794
Q10.1961722488
median0.2068723703
Q30.2179802956
95-th percentile0.2328903988
Maximum2.152046784
Range2.091358378
Interquartile range (IQR)0.02180804676

Descriptive statistics

Standard deviation0.04637534997
Coefficient of variation (CV)0.2237268965
Kurtosis1463.301335
Mean0.2072855374
Median Absolute Deviation (MAD)0.01092311446
Skewness34.91692343
Sum438.8234826
Variance0.002150673084
MonotonicityNot monotonic
2021-06-18T15:33:57.691107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21698113213
 
0.1%
0.20512820513
 
0.1%
0.20930232563
 
0.1%
0.19444444443
 
0.1%
0.2123
 
0.1%
0.21428571433
 
0.1%
0.22222222223
 
0.1%
0.2322
 
0.1%
0.20168067232
 
0.1%
0.18947368422
 
0.1%
Other values (2058)2090
98.7%
ValueCountFrequency (%)
0.06068840581
< 0.1%
0.11865014831
< 0.1%
0.11887286191
< 0.1%
0.11984403861
< 0.1%
0.12110595591
< 0.1%
0.12916099771
< 0.1%
0.13021168591
< 0.1%
0.13468385381
< 0.1%
0.13821138211
< 0.1%
0.13861854291
< 0.1%
ValueCountFrequency (%)
2.1520467841
< 0.1%
0.4690431521
< 0.1%
0.30653266331
< 0.1%
0.26829268291
< 0.1%
0.26771653541
< 0.1%
0.26200873361
< 0.1%
0.25795356841
< 0.1%
0.25751072961
< 0.1%
0.25721455461
< 0.1%
0.25440313111
< 0.1%

Anzahl der Kinder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.63923949
Minimum1.32
Maximum2.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2021-06-18T15:33:59.270251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile1.49
Q11.57
median1.63
Q31.7
95-th percentile1.81
Maximum2.3
Range0.98
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.1032438786
Coefficient of variation (CV)0.06298279128
Kurtosis2.170933799
Mean1.63923949
Median Absolute Deviation (MAD)0.06
Skewness0.8016132634
Sum3470.27
Variance0.01065929848
MonotonicityNot monotonic
2021-06-18T15:33:59.721667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.692
 
4.3%
1.6489
 
4.2%
1.6289
 
4.2%
1.6388
 
4.2%
1.6787
 
4.1%
1.6186
 
4.1%
1.5983
 
3.9%
1.6578
 
3.7%
1.5877
 
3.6%
1.6976
 
3.6%
Other values (58)1272
60.1%
ValueCountFrequency (%)
1.321
 
< 0.1%
1.372
 
0.1%
1.42
 
0.1%
1.412
 
0.1%
1.421
 
< 0.1%
1.438
0.4%
1.449
0.4%
1.4516
0.8%
1.4612
0.6%
1.4718
0.9%
ValueCountFrequency (%)
2.31
 
< 0.1%
2.271
 
< 0.1%
2.171
 
< 0.1%
2.091
 
< 0.1%
2.051
 
< 0.1%
2.041
 
< 0.1%
2.022
 
0.1%
23
0.1%
1.972
 
0.1%
1.965
0.2%

Interactions

2021-06-18T15:33:30.447310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:30.751038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:31.028364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:31.270516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:31.488705image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:31.671796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:31.876642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:32.125335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:32.289865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:32.458112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:32.642252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:32.825569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:33.034843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:33.294271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:33.479322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:33.650884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:33.848300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:34.026612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:34.218650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:35.433511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:35.717327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:35.986239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:36.247875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:36.463778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:36.657063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:36.883868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.050159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.207863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.392117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.596009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.768203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:37.942712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.108855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.290540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.467491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.638494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.809206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:38.967333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:39.139102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:39.291191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:39.499385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:39.729366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:39.909369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.117748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.294246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.453585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.634000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.807532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:40.963545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:41.141186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:41.378278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:41.681895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:41.892590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:42.086771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:42.255993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:42.456268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:42.691689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:42.869154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.053660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.221805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.396507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.590818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.772277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:43.956215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:44.129184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:44.315529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:44.489549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:44.682536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:44.863791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:45.055549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:45.252931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:45.507068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:45.801388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:46.040240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:46.420388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:46.733137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:46.993372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:47.298037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:47.575686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:47.921508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-18T15:33:48.196458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-18T15:34:00.105070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-18T15:34:00.529311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-18T15:34:00.927360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-18T15:34:01.346640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-18T15:33:48.804218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-18T15:33:49.636268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-18T15:33:50.024276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NameEinwohnerArbeitsstättenBeschäftigteAlterEinkommenErbwerbstätige (15-64)GrundstückspreiseAnzahl an EhepaarenAnzahl der Kinder
0Gramais410.1707320.29268348.7437.6240.46341568.40.2682931.50
1Namlos650.1384620.24615447.7040.1660.64615463.60.2461541.50
2Tschanigraben670.0149250.01492551.3435.9460.38806011.90.1940301.78
3Kaisers780.1794870.25641044.3542.8250.47435968.40.1538462.00
4Hinterhornbach940.1276600.21276644.5333.7190.48936268.40.2021281.62
5Spiss990.0909090.14141446.2327.4080.646465154.40.1818181.46
6Pfafflar1050.1428570.20952447.8840.8140.54285763.60.1714291.75
7Großhofen1060.1698110.47169841.5141.1860.509434125.30.1886791.67
8Andlersdorf1270.1338580.27559146.8844.2630.543307132.30.2677171.79
9Dünserberg1400.1500000.29285741.9630.8650.585714230.50.2214291.92

Last rows

NameEinwohnerArbeitsstättenBeschäftigteAlterEinkommenErbwerbstätige (15-64)GrundstückspreiseAnzahl an EhepaarenAnzahl der Kinder
2107Wien-Simmering1050220.0414680.37242739.4238.2370.434071613.80.1660891.69
2108Wien-Leopoldstadt1052370.0824420.73796340.1243.9640.450003NaN0.1386961.68
2109Wien-Liesing1118120.0704310.55387642.3246.2370.420957700.60.1690961.64
2110Innsbruck1310590.0992530.79066742.3540.1050.4768161066.20.1389831.59
2111Salzburg1554160.1020810.75889243.6142.8250.4692051043.00.1584011.60
2112Wien-Floridsdorf1739160.0433600.35270540.7741.9170.415931705.70.1590311.68
2113Wien-Donaustadt1988060.0490730.34068940.4044.8710.445067759.00.1707391.65
2114Linz2065370.0800870.88315442.4542.7590.478011354.70.1579331.65
2115Wien-Favoriten2105730.0451720.38749539.9137.0700.408913707.10.1599401.71
2116Graz2911340.0848610.70529041.1942.7160.478790246.30.1447961.62